Reinforcement Learning Methods in Speech and Language Technology / by Baihan Lin.

Format:

Book

Author/Creator:

Lin, Baihan, author.

Series:

Signals and Communication Technology, 1860-4870

Language:

English

Subjects (All):

Signal processing.

Natural language processing (Computer science).

Computational linguistics.

Machine learning.

Signal, Speech and Image Processing.

Natural Language Processing (NLP).

Computational Linguistics.

Machine Learning.

Local Subjects:

Signal, Speech and Image Processing.

Natural Language Processing (NLP).

Computational Linguistics.

Machine Learning.

Physical Description:

1 online resource (205 pages)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

This book offers a comprehensive guide to reinforcement learning (RL) and bandits methods, specifically tailored for advancements in speech and language technology. Starting with a foundational overview of RL and bandit methods, the book dives into their practical applications across a wide array of speech and language tasks. Readers will gain insights into how these methods shape solutions in automatic speech recognition (ASR), speaker recognition, diarization, spoken and natural language understanding (SLU/NLU), text-to-speech (TTS) synthesis, natural language generation (NLG), and conversational recommendation systems (CRS). Further, the book delves into cutting-edge developments in large language models (LLMs) and discusses the latest strategies in RL, highlighting the emerging fields of multi-agent systems and transfer learning. Emphasizing real-world applications, the book provides clear, step-by-step guidance on employing RL and bandit methods to address challenges in speech and language technology. It includes case studies and practical tips that equip readers to apply these methods to their own projects. As a timely and crucial resource, this book is ideal for speech and language researchers, engineers, students, and practitioners eager to enhance the performance of speech and language systems and to innovate with new interactive learning paradigms from an interface design perspective. Provides a comprehensive survey of reinforcement learning methods tailored to speech and language technology; Discusses real-world application studies such as ASR, TTS, large language models, and conversational systems; Covers emerging trends in deep reinforcement learning, multi-agent systems, and transfer learning.

Contents:

Part I. A New Learning Paradigm in Speech and Language Technology

Chapter 1. RL+SLT: Emerging RL-Powered Speech and Language Technologies

Chapter 2. Why is RL+SLT Important, Now and How?

Part II. Bandits and Reinforcement Learning: A Gentle Introduction

Chapter 3. Introduction to the Bandit Problems

Chapter 4. Reinforcement Learning: Preliminaries and Terminologies

Chapter 5. The RL Toolkit: A Spectrum of Algorithms

Chapter 6. Inverse Reinforcement Learning Problem

Chapter 7. Behavioral Cloning and Imitation Learning

Part III. Reinforcement Learning in SLT Applications

Chapter 8. Reinforcement Learning Formulations for Speech and Language Applications

Chapter 9. Reinforcement Learning in Automatic Speech Recognition (ASR): The Voice-First Revolution

Chapter 10. Reinforcement Learning in Speaker Recognition and Diarization: Decoding the Voices in the Crowd

Chapter 11. Reinforcement Learning in Natural Language Understanding (NLU): Teaching Machines to Comprehend

Chapter 12. Reinforcement Learning in Spoken Language Understanding (SLU): Giving Machines an Ear for Understanding

Chapter 13. Reinforcement Learning in Text-to-Speech (TTS) Synthesis: Giving Machines a Voice

Chapter 14. Reinforcement Learning in Natural Language Generation (NLG): Machines as Wordsmiths

Chapter 15. Reinforcement Learning in Large Language Models (LLM): The Rise of AI Language Giants

Chapter 16. Reinforcement Learning in Conversational Recommendation Systems (CRS): AI’s Personal Touch

Part IV. Advanced Topics and Future Avenues

Chapter 17. Emerging Strategies in Reinforcement Learning Methods

Chapter 18. Navigating the Frontiers: Key Challenges and Opportunities in RL-Powered Speech and Language Technology

Chapter 19. Reflections, Resources, and Future Horizons in RL+SLT.

Notes:

Includes bibliographical references and index.

ISBN:

9783031537202

3031537203